<i>In Vivo</i>Detection of the Effect of Electroacupuncture on “Zusanli” Acupoint in Rats with Adjuvant-Induced Arthritis through Optical Coherence Tomography

Zhong, Huiqing; Yang, Hui; Zhou, Yongan; Guo, Zhouyi; Su, Chengkang; Jia, Liping; Lin, Jinrong; Jiang, Xuemei

doi:10.1155/2016/2681463

Cited by 3 publications

References 32 publications

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Deep Learning‐Based Single‐Cell Optical Image Studies

Sun

Tárnok

2020

Cytometry Pt A

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Optical imaging technology that has the advantages of high sensitivity and cost‐effectiveness greatly promotes the progress of nondestructive single‐cell studies. Complex cellular image analysis tasks such as three‐dimensional reconstruction call for machine‐learning technology in cell optical image research. With the rapid developments of high‐throughput imaging flow cytometry, big data cell optical images are always obtained that may require machine learning for data analysis. In recent years, deep learning has been prevalent in the field of machine learning for large‐scale image processing and analysis, which brings a new dawn for single‐cell optical image studies with an explosive growth of data availability. Popular deep learning techniques offer new ideas for multimodal and multitask single‐cell optical image research. This article provides an overview of the basic knowledge of deep learning and its applications in single‐cell optical image studies. We explore the feasibility of applying deep learning techniques to single‐cell optical image analysis, where popular techniques such as transfer learning, multimodal learning, multitask learning, and end‐to‐end learning have been reviewed. Image preprocessing and deep learning model training methods are then summarized. Applications based on deep learning techniques in the field of single‐cell optical image studies are reviewed, which include image segmentation, super‐resolution image reconstruction, cell tracking, cell counting, cross‐modal image reconstruction, and design and control of cell imaging systems. In addition, deep learning in popular single‐cell optical imaging techniques such as label‐free cell optical imaging, high‐content screening, and high‐throughput optical imaging cytometry are also mentioned. Finally, the perspectives of deep learning technology for single‐cell optical image analysis are discussed. © 2020 International Society for Advancement of Cytometry

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Deep Learning‐Based Single‐Cell Optical Image Studies

Sun

Tárnok

2020

Cytometry Pt A

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Microphysiological System for High-Throughput Computer Vision Measurement of Microtissue Contraction

et al. 2021

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The ability to measure microtissue contraction in vitro can provide important information when modeling cardiac, cardiovascular, respiratory, digestive, dermal, and skeletal tissues. However, measuring tissue contraction in vitro often requires the use of high number of cells per tissue construct along with time-consuming microscopy and image analysis. Here, we present an inexpensive, versatile, high-throughput platform to measure microtissue contraction in a 96-well plate configuration using one-step batch imaging. More specifically, optical fiber microprobes are embedded in microtissues, and contraction is measured as a function of the deflection of optical signals emitted from the end of the fibers. Signals can be measured from all the filled wells on the plate simultaneously using a digital camera. An algorithm uses pixel-based image analysis and computer vision techniques for the accurate multiwell quantification of positional changes in the optical microprobes caused by the contraction of the microtissues. Microtissue constructs containing 20,000–100,000 human ventricular cardiac fibroblasts (NHCF-V) in 6 mg/mL collagen type I showed contractile displacements ranging from 20–200 μm. This highly sensitive and versatile platform can be used for the high-throughput screening of microtissues in disease modeling, drug screening for therapeutics, physiology research, and safety pharmacology.

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